Magnetic resonance imaging method, device, medical device and storage medium

ABSTRACT

Provided are a method and device for magnetic resonance parameter imaging, medical equipment, and a storage medium. The method comprises: performing acceleration sampling with respect to an image to be reconstructed of an observation target in a preset parameter direction to acquire K-space data corresponding to the image to be reconstructed, calculating, on the basis of the K-space data and of a parameter relaxation model, a parameter value and a compensation coefficient of the image to be reconstructed, generating, on the basis of the compensation coefficient, a compensation image corresponding to the image to be reconstructed, calculating, on the basis of the compensation image, respectively a low-rank part and a sparse part of the image to be reconstructed so as to update the compensation image.

TECHNICAL FIELD

The invention belongs to the field of magnetic resonance imagingtechnology, and particularly involves a magnetic resonance imagingmethod, device, medical device and storage medium.

BACKGROUND

Quantitative magnetic resonance imaging differentiates different tissuesby quantitative analysis of some intrinsic parameters of differenttissues in the human body, such as, longitudinal relaxation time T₁,transverse relaxation time T₂, proton density, and longitudinalrelaxation time in the rotating frame T_(1ρ), to provide more accuratediagnostic information for doctors, so it has been widely applied toclinical practice. However, during the magnetic resonance imaging,images of multiple different parameter direction values (i.e. TE (echotime) and TSL (spin-lock time)) needs to be acquired, so it requireslong scan time, which has become a bottleneck restricting the clinicaldevelopment of quantitative magnetic resonance imaging.

In order to reduce the scan time, the current commercial fast imagingtechniques mainly include partial Fourier (PF) and parallel imaging(i.e. sensitivity encoding (SENSE) and Generalized AutocalibratingPartially Parallel Acquisition (GRAPPA)). In recent years, compressedsensing technology based on sparse sampling theory has been widelyconcerned and applied. These techniques aim to obtain a similarparameter map or a parameter map without obvious artifacts by developingredundancy in image or K-space data. Therefore, the quality of theobtained parameter map is highly dependent on the parametric imagingmethod adopted. Traditional rapid parametric imaging methods usuallycomprise two phases: reconstruction and fitting. In the reconstructionphase, a parameter-weighted image is reconstructed from undersampleddata. In the fitting phase, the parameter image is obtained by fittingfrom the reconstructed the parameter-weighted image through theestablished relaxation model. However, there are some errors between thereconstructed parameter-weighted image and the actual image. Theseerrors are transmitted to the subsequent fitting phase and furtheraffect the fitted image.

SUMMARY OF THE INVENTION

The invention provides a magnetic resonance imaging method, device,medical device and storage medium, aiming at solving the problems oflong scan time and low imaging precision of the magnetic resonanceimaging method with the existing technology.

On the one hand, the invention provides a magnetic resonance imagingmethod, which comprises the following steps:

conducting accelerated sampling on the image of the preset observedtarget to be reconstructed under the preset parameter direction toobtain the corresponding K-space data of the said image to bereconstructed;

calculating the initial parameter value and compensation coefficient ofthe said image to be reconstructed according to the said K-spacecenterdata and the preset parametric relaxation model;

generating the corresponding compensated image of the said image to bereconstructed according to the said compensation coefficient andcalculate the low-rank component and the sparse component of the saidimage to be reconstructed according to the said compensated image;

updating the compensated image according to the said low-rank componentand the said sparse component, and judge whether the update of the saidcompensated image converges. If the update converges, updating the saidimage to be reconstructed according to the said updated compensatedimage; otherwise, skipping to the step of calculating the low-rankcomponent and the sparse component of the said image to be reconstructedrespectively;

judging whether the update of the said image to be reconstructedconverges. If the update converges, fitting and outputting the parametermap of the said observed target according to the said parametricrelaxation model and the updated image to be reconstructed, otherwise,calculating the parameter value and compensation coefficient of theupdated image to be reconstructed according to the said parametricrelaxation model, and skipping to the step of generating thecorresponding compensated image of the said image to be reconstructedaccording to the said compensation coefficient.

On the other hand, the invention provides a magnetic resonance imagingdevice, which comprises:

Accelerated sampling unit is used to conduct accelerated sampling on theimage of the preset observed target to be reconstructed under the presetparameter direction to obtain the corresponding K-space data of the saidimage to be reconstructed;

Coefficient calculation unit is used to calculate the parameter valueand compensation coefficient of the said image to be reconstructedaccording to the said K-space data and the preset parametric relaxationmodel;

Image compensation unit is used to generate the correspondingcompensated image of the said image to be reconstructed according to thesaid compensation coefficient and calculate the low-rank component andthe sparse component of the said image to be reconstructed according tothe compensated image;

Image update unit is used to update the compensated image according tothe said low-rank component and the said sparse component, and judgewhether the update of the said compensated image converges. If theupdate converges, the said image to be reconstructed is updatedaccording to the said updated compensated image; otherwise, the imagecompensation unit is triggered to execute the step of calculating thelow-rank component and the sparse component of the said image to bereconstructed respectively; and

Fitting and outputting unit is used to judge whether the update of thesaid image to be reconstructed converges. If the update converges, theparameter map of the said observed target is fitted and output accordingto the said parametric relaxation model and the said updated image to bereconstructed, otherwise the parameter value and compensationcoefficient of the updated image to be reconstructed is calculatedaccording to the said parametric relaxation model, and the said imagecompensation unit is triggered to execute the step of generating thecorresponding compensated image of the said image to be reconstructedaccording to the said compensation coefficient.

In addition, the invention also provides a medical device, whichcomprises a memory, a processor, and a computer program stored in thememory and capable of running on the said processor, which executes thesaid computer program to implement said steps of the aforesaid magneticresonance imaging method.

Moreover, the invention also provides a computer readable storagemedium, which stores a computer program, which is executed by theprocessor to implement the said steps of the aforesaid magneticresonance imaging method.

The method comprises the following steps: conducting acceleratedsampling on the image of the observed target to be reconstructed underthe preset parameter direction to obtain the corresponding K-space dataof the image to be reconstructed, calculating the parameter value andcompensation coefficient of the image to be reconstructed according tothe K-space data and the parametric relaxation model, generating thecorresponding compensated image of the image to be reconstructedaccording to the compensation coefficient, calculating the low-rankcomponent and the sparse component of the image to be reconstructedaccording to the compensated image to update the compensated image forseveral times, updating the image to be reconstructed according to theupdated compensated image, fitting and outputting the parameter map ofthe observed target when the update of the image to be reconstructedconverges, or calculating the parameter value and compensationcoefficient of the updated image to be reconstructed according to theparametric relaxation model, and skipping to the steps to generate thecorresponding compensated image of the image to be reconstructedaccording to the compensation coefficient, thus to effectively improvethe efficiency and accuracy of magnetic resonance imaging.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an implementation flow chart of the magnetic resonance imagingmethod provided in embodiment 1 of the invention;

FIG. 2 is a structural diagram of the magnetic resonance imaging deviceprovided in embodiment 2 of the invention;

FIG. 3 is an optimal structural diagram the magnetic resonance imagingdevice provided in embodiment 2 of the invention; and

FIG. 4 is the structure diagram of the medical device provided inembodiment 3 of the invention.

DETAILED DESCRIPTIONS

In order to make the objectives, technical solutions, and advantages ofthe present invention clearer, the present invention is furtherdescribed in detail below with reference to the accompanying figures andembodiments. It should be appreciated that the specific embodimentsdescribed herein are merely illustrative of the present invention; it isnot intended to limit the present invention.

The detailed description of the implementation of the invention made inaccordance with embodiments is as follows:

Embodiment 1

FIG. 1 shows the implementation process of the magnetic resonanceimaging method provided in embodiment 1 of the invention. For theconvenience of illustration, only parts related to the embodiments ofthe invention are shown as follows:

In step S101, conducting accelerated sampling on the image of the presetobserved target to be reconstructed under the preset parameter directionto obtain the corresponding K-space data of the image to bereconstructed.

In the embodiment of the invention, the observed object may be apatient's tissues and organs, and the parameter directions may be echotime (TE) and spin-lock time (TSL). When the parameter direction is TE,the parameter value and parameter map obtained by subsequent fitting areT₂ value and T₂ map. When the parameter direction is TSL, the parametervalue and parameter map obtained by subsequent fitting are T_(1ρ) valueand T_(1ρ) map.

In the embodiment of the invention, full sampling can be conducted inthe frequency-encoding direction and variable-density undersampling canbe conducted in the phase-encoding direction of the observed object toobtain the preset number of K-space data corresponding to the image tobe reconstructed. K-space data are undersampled data, so thephase-parameter of the accelerated sampling for magnetic resonanceimaging conforms to the random sampling theory of compressed sensing,wherein different images to be reconstructed correspond to differentparameter direction values. For example, in the TSL direction, differentimages to be reconstructed correspond to different TSL values.

As an example, in the TSL direction, K-space data of the image to bereconstructed under different TSL values obtained by full sampling inthe frequency-encoding direction and variable-density undersampling inthe phase-encoding direction are undersampled data.

In step S102, calculating the parameter value and compensationcoefficient of the image to be reconstructed according to the K-spacedata and the preset parametric relaxation model.

In the embodiment of the invention, after obtaining the K-space dataunder different parameter direction values by sampling, the fullsampling part of the K-space data center can be converted to the imagedomain to obtain the corresponding image of the full sampling part ofthe K-space data center. The parameter value of the image to bereconstructed is obtained by fitting according to the correspondingimage of the full sampling part and the parametric relaxation model.Then the compensation coefficient of the image to be reconstructed iscalculated according to the parameter value. The calculated parametervalue and compensation coefficient are both initial values, wherein,different parameter directions correspond to different parametricrelaxation models.

As an example, when the parameter direction is TSL, the full samplingpart of the K-space data center corresponding to different TSLs is beconverted to the image domain. The T_(1ρ) relaxation model is fittedwith the image converted from the full sampling part of the K-space datacenter to obtain the T_(1ρ) value of the image to be reconstructed. TheT_(1ρ) relaxation model can be indicated as:

M_(x)=M₀ exp(−TSL_(k)/T_(1ρ) ^(i)), where, T_(1ρ) ^(i) is the T_(1ρ)value of the image to be reconstructed in the i^(th) update of the imageto be reconstructed, M_(x) is the image intensity of the image to bereconstructed under TSL_(k), the k^(th) spin-lock time, M₀ is theequilibrium image intensity obtained without applying the spin-lockpulse, k=1, 2, . . . . Specifically, the logarithm of both sides of thisequation can be taken first to transform T_(1ρ) relaxation model into alinear equation, i.e. a linear function of TSL, and then all pixels ofthe image to be reconstructed along the TSL direction can be fitted. Thecompensation coefficient of the image to be reconstructed is calculatedaccording to the calculated T_(1ρ) value. The formula can be Coef^(i)exp(TSL_(k)/T_(1ρ) ^(i)), where, Coef^(i) is the compensationcoefficient of the image to be reconstructed in the i^(th) update of theimage to be reconstructed.

In the embodiment of the invention, as the parameter value of the imageto be reconstructed is calculated according to the full sampling part ofthe K-space data center, its resolution is very low, so it requiresiterative updates. In the subsequent iteration, the parameter values canbe calculated directly according to the updated image to bereconstructed.

In step S103, generating the corresponding compensated image of theimage to be reconstructed according to the compensation coefficient.

In the embodiment of the invention, the compensation coefficient can bemultiplied by each pixel of the image to be reconstructed to obtain thecompensated image of the image to be reconstructed. The compensatedimage can be indicated as U^(i)=C(X^(i-1)), and C(•) is the compensationoperator, wherein, X^(i-1) is the image sequence composed of the imageto be reconstructed in the i^(th) update of the image to bereconstructed.

In step S104, calculating the low-rank component and the sparsecomponent of the image to be reconstructed according to the compensatedimage.

In the embodiment of the invention, after generating the correspondingcompensated image of the image to be reconstructed, the low-rankcomponent of the image to be reconstructed can be calculated accordingto the compensated image, the preset singular value thresholdingoperator and the preset initial value of the sparse component of theimage to be reconstructed. Then the sparse component of the image to bereconstructed is updated according to the compensated image, thelow-rank component of the image to be reconstructed and the preset softthresholding operator, wherein the initial value of the sparse componentof the image to be reconstructed can be preset as 0.

Preferably, the formula of the low-rank component L_(j) is indicated as:

L_(j)=SVT(U_(j-1) ^(u)−S_(j-1)) where, SVT(•) is the singular valuethresholding operator, the calculation process of which can be indicatedas SVT_(λ)(M)=UΛ_(λ)(Σ)V^(H), M=UΣV^(H) represents singular valuedecomposition (SVD), U and V are matrices composed of left and rightsingular values respectively, V^(H) is the conjugate transpose of V, Σis the diagonal matrix composed of singular values of M, Λ_(λ)(Σ)represents that the maximum singular value in Σ remains unchanged whileothers are 0, L_(j) is the low-rank component of the image to bereconstructed in the j^(th) update of the compensated image, S_(j-1) isthe sparse component of the image to be reconstructed before the j^(th)update of the compensated image, and U_(j-1) ^(i) is the compensatedimage before the j^(th) update of the compensated image in the i^(th)update of the image to be reconstructed.

Preferably, the formula of the sparse component S_(j) is indicated as:

S_(j)=ST (U_(j-1) ^(i)−L_(j)), wherein ST(•) is the soft thresholdingoperator, the calculation process of which can be indicated as

${{{ST}(p)} = {\frac{p}{p}{\max \left( {0,{{p - v}}} \right)}}},$

p represents an element in the image matrix, v is the presetthresholding, and S_(j) is the sparse component after the j^(th) updateof the compensated image.

In step S105, updating the compensated image according to the low-rankcomponent and the sparse component.

In the embodiment of the invention, after calculating the low-rankcomponent and the sparse component of the image to be reconstructed, thecompensated image can be updated according to the low-rank component andthe sparse component to update the data fidelity term of the compensatedimage. Preferably, the updating formula of the compensated image is:

U_(j) ^(i)=L_(j)+S_(j)−C(E*(EC⁻¹(L_(j)+S_(j)))−d), where, U_(j) ^(i) isthe compensated image obtained after the j^(th) update of thecompensated image in the i^(th) update of the image to be reconstructed,E is the preset encoding matrix, E* is the conjugate transpose of E,E(X)=d, X is the image sequence composed of the image to bereconstructed.

In step S106, judging whether the update of the compensated imageconverges.

In the embodiment of the invention, whether the update of thecompensated image converges can be judged by judging whether the currentnumber of updates of the compensated image reaches the preset firstnumber thresholding, or by judging whether the difference between thecompensated images before and after update is less than the preset firstdifference thresholding.

In the embodiment of the invention, if the update of the compensatedimage converges, step S107 is executed, otherwise, step S104 is executedto calculate the low-rank component and the sparse component of the saidimage to be reconstructed respectively.

In step S107, updating the image to be reconstructed according to theupdated compensated image,

In the embodiment of the invention, the image to be reconstructed can beupdated by dividing each pixel in the updated compensated image by thecompensation coefficient. The updating formula can be indicated as:

X^(i)=C⁻¹(U_(j) ^(i)), X^(i) is the updated image to be reconstructedafter the i^(th) update of the image to be reconstructed.

In step S108, judging whether the update of the image to bereconstructed converges.

In the embodiment of the invention, whether the update of the image tobe reconstructed converges can be judged by judging whether the currentnumber of updates of the image to be reconstructed reach the presetsecond number thresholding, or by judging whether the difference betweenthe images to be reconstructed before and after update is less than thepreset second difference thresholding.

In the embodiment of the invention, if the update of the image to bereconstructed converges, step S109 is executed, otherwise, step S110 isexecuted.

In step S109, fitting and outputting the parameter map of the observedtarget according to the parametric relaxation model and the updatedimage to be reconstructed.

In the embodiment of the invention, if the update of the image to bereconstructed converges, the updated image to be reconstructed is fittedaccording to the parametric relaxation model to obtain correspondingparameter values of the updated image to be reconstructed. The parametermap of the observed target composed of parameter values are output, andthe parameter map is the final image of the image to be reconstructed.

In step S110, calculating the parameter value and compensationcoefficient of the updated image to be reconstructed according to theparametric relaxation model.

In the embodiment of the invention, if the update of the image to bereconstructed does not converge, the updated image to be reconstructedand the parametric relaxation model are fitted to obtain the parametervalues of the updated image to be reconstructed. Then the compensationcoefficient is calculated and updated according to the parameter valueof the updated image to be reconstructed. Step S103 is executed togenerate the corresponding compensated image of the image to bereconstructed according to the compensation coefficient, to continueupdating the image to be reconstructed.

In the embodiment of the invention, the scanning speed and imaging speedof magnetic resonance imaging are accelerated through variable-densityundersampling, parameters in the fitting process are added to thereconstruction process to guide the reconstruction of the image to bereconstructed, and the reconstruction process and the fitting process ofmagnetic resonance imaging are connected, which effectively improves theaccuracy and efficiency of magnetic resonance imaging.

Embodiment 2

FIG. 2 shows the structure of the magnetic resonance imaging deviceprovided in embodiment 2 of the invention. For the convenience ofillustration, only parts related to the embodiments of the invention areshown as follows:

Accelerated sampling unit 21 is used to conduct accelerated sampling onthe image of the preset observed target to be reconstructed under thepreset parameter direction to obtain the corresponding K-space data ofthe image to be reconstructed.

In the embodiment of the invention, the parameter directions may be echotime (TE) and spin-lock time (TSL). When the parameter direction is TE,the parameter value and parameter map obtained by subsequent fitting areT₂ value and T₂ map. When the parameter direction is TSL, the parametervalue and parameter map obtained by subsequent fitting are T_(1ρ) valueand T_(1ρ) map.

In the embodiment of the invention, full sampling can be conducted inthe frequency-encoding direction and variable-density undersampling canbe conducted in the phase-encoding direction of the observed object toobtain the preset number of K-space data corresponding to the image tobe reconstructed. K-space data are undersampled data, so thephase-parameter of the accelerated sampling for magnetic resonanceimaging conforms to the random sampling theory of compressed sensing. Asan example, in the TSL direction, K-space data of the image to bereconstructed under different TSL values can be obtained by fullsampling in the frequency-encoding direction and variable-densityundersampling in the phase-encoding direction.

Coefficient calculation unit 22 is used to calculate the parameter valueand compensation coefficient of the image to be reconstructed accordingto the K-space data and the preset parametric relaxation model.

In the embodiment of the invention, after obtaining the K-space dataunder different parameter direction values by sampling, the fullsampling part of the K-space data center can be converted to the imagedomain to obtain the corresponding image of the full sampling part ofthe K-space data center. The parameter value of the image to bereconstructed is obtained by fitting according to the correspondingimage of the full sampling part and the parametric relaxation model.Then the compensation coefficient of the image to be reconstructed iscalculated according to the parameter value. The calculated parametervalue and compensation coefficient are both initial values.

As an example, when the parameter direction is TSL, the full samplingpart of the K-space data center corresponding to different TSLs is beconverted to the image domain. The T_(1ρ) relaxation model is fittedwith the image converted from the full sampling part of the K-space datacenter to obtain the T_(1ρ) value of the image to be reconstructed. TheT_(1ρ) relaxation model can be indicated as:

M_(x)=M₀ exp(−TSL_(k)/T_(1ρ) ^(i)), where, T_(1ρ) ^(i) is the T_(1ρ)value of the image to be reconstructed in the i^(th) update of the imageto be reconstructed, M_(x) is the image intensity of the image to bereconstructed under TSL_(k), the k^(th) spin-lock time, M₀ is theequilibrium image intensity obtained without applying the spin-lockpulse, k=1, 2, . . . . Specifically, the logarithm of both sides of thisequation can be taken first to transform T_(1ρ) relaxation model into alinear equation, i.e. a linear function of TSL, and then all pixels ofthe image to be reconstructed along the TSL direction can be fitted. Thecompensation coefficient of the image to be reconstructed is calculatedaccording to the calculated T_(1ρ). The formula can beCoef^(i)=exp(TSL_(k)/T_(1ρ) ^(i), where, Coef^(i) is the compensationcoefficient of the image to be reconstructed in the i^(th) update of theimage to be reconstructed.

In the embodiment of the invention, as the parameter value of the imageto be reconstructed is calculated according to the full sampling part ofthe K-space data center, its resolution is very low, so it requiresiterative updates. In the subsequent iteration, the parameter values canbe calculated directly according to the updated image to bereconstructed.

Image compensation unit 23 is used to generate the correspondingcompensated image of the image to be reconstructed according to thecompensation coefficient and calculate the low-rank component and thesparse component of the image to be reconstructed according to thecompensated image.

In the embodiment of the invention, the compensation coefficient can bemultiplied by each pixel of the image to be reconstructed to obtain thecompensated image of the image to be reconstructed. The compensatedimage can be indicated as U^(i)=C(X^(i-1)), and C(•) is the compensationoperator, wherein X^(i-1) is the image sequence composed of the image tobe reconstructed in the i^(th) update of the image to be reconstructed.Then, the low-rank component of the image to be reconstructed can becalculated according to the compensated image, the preset singular valuethresholding operator and the preset initial value of the sparsecomponent of the image to be reconstructed. Then the sparse component ofthe image to be reconstructed is updated according to the compensatedimage, the low-rank component of the image to be reconstructed and thepreset soft thresholding operator.

Preferably, the formula of the low-rank component L_(j) is indicated as:

L_(j)=SVT(U_(j-1) ^(i)−S_(j-1)), wherein SVT(•) is the singular valuethresholding operator, the calculation process of which can be indicatedas SVT_(λ)(M)=UΛ_(λ)(Σ)V^(H), M=UΣV^(H) represents singular valuedecomposition (SVD), U and V are matrices composed of left and rightsingular values respectively, V^(H) is the conjugate transpose of V, Σis the diagonal matrix composed of singular values of M, Λ_(λ)(Σ)represents that the maximum singular value in Σ remains unchanged whileothers are 0, L_(j) is the low-rank component of the image to bereconstructed in the j^(th) update of the compensated image, S_(j-1) isthe sparse component of the image to be reconstructed before the j^(th)update of the compensated image, and U_(j-1) ^(i) is the compensatedimage before the j^(th) update of the compensated image in the i^(th)update of the image to be reconstructed.

Preferably, the formula of the sparse component S_(j) is indicated as:

S_(j)=ST(U_(j-1) ^(i)−L_(j)), where, ST(•) is the soft thresholdingoperator, the calculation process of which can be indicated as

${{{ST}(p)} = {\frac{p}{p}{\max \left( {0,{{p - v}}} \right)}}},$

p represents an element in the image matrix, v is the presetthresholding, and S_(j) is the sparse component after the j^(th) updateof the compensated image.

Image update unit 24 is used to update the compensated image accordingto the low-rank component and the sparse component, and judge whetherthe update of the compensated image converges. If the update converges,the image to be reconstructed is updated according to the updatedcompensated image; otherwise, image compensation unit 23 is triggered toexecute the step of calculating the low-rank component and the sparsecomponent of the image to be reconstructed respectively.

In the embodiment of the invention, after calculating the low-rankcomponent and the sparse component of the image to be reconstructed, thecompensated image can be updated according to the low-rank component andthe sparse component to update the data fidelity term of the compensatedimage. Preferably, the updating formula of the compensated image is:

U_(j) ^(i)=L_(j)+S_(j)−C(E*(EC⁻¹(L_(j)+S_(j)))−d), where, U_(j) ^(i) isthe compensated image obtained after the j^(th) update of thecompensated image in the i^(th) update of the image to be reconstructed,E is the preset encoding matrix, E* is the conjugate transpose of E,E(X)=d, X is the image sequence composed of the image to bereconstructed.

In the embodiment of the invention, whether the update of thecompensated image converges can be judged by judging whether the currentnumber of updates of the compensated image reaches the preset firstnumber thresholding, or by judging whether the difference between thecompensated images before and after update is less than the preset firstdifference thresholding.

In the embodiment of the invention, if the update of the compensatedimage converges, the image to be reconstructed can be updated bydividing each pixel in the updated compensated image by the compensationcoefficient. The updating formula can be indicated as:

X^(i)=C⁻¹(U_(j) ^(i)), X^(i) is the updated image to be reconstructed inthe i^(th) update of the image to be reconstructed. If the update of thecompensated image does not converge, image compensation unit 23 istriggered to execute the step of calculating the low-rank component andthe sparse component of the image to be reconstructed respectively.

Fitting and outputting unit 25 is used to judge whether the update ofthe image to be reconstructed converges. If the update converges, theparameter map of the observed target is fitted and output according tothe parametric relaxation model and the updated image to bereconstructed, otherwise, the parameter value and compensationcoefficient of the updated image to be reconstructed is calculatedaccording to the parametric relaxation model, and image compensationunit 23 is triggered to execute the step of generating the correspondingcompensated image of the image to be reconstructed according to thecompensation coefficient.

In the embodiment of the invention, whether the update of the image tobe reconstructed converges can be judged by judging whether the currentnumber of updates of the image to be reconstructed reach the presetsecond number thresholding, or by judging whether the difference betweenthe images to be reconstructed before and after update is less than thepreset second difference thresholding.

In the embodiment of the invention, if the update of the image to bereconstructed converges, the updated image to be reconstructed is fittedaccording to the parametric relaxation model to obtain correspondingparameter values of the updated image to be reconstructed. The parametermap of the observed target composed of parameter values are output, andthe parameter map is the final image of the image to be reconstructed.If the update of the image to be reconstructed does not converge, theupdated image to be reconstructed and the parametric relaxation modelare fitted to obtain the parameter values of the updated image to bereconstructed. Then the compensation coefficient is calculated andupdated according to the parameter value of the updated image to bereconstructed. Image compensation unit 23 is triggered to execute thestep of generating the corresponding compensated image of the image tobe reconstructed according to the compensation coefficient.

Preferably, as shown in FIG. 3, accelerated sampling unit 21 comprises:

Variable-density undersampling unit 311 is used to conduct full samplingin the frequency-encoding direction and variable-density undersamplingin the phase-encoding direction of the observed object to obtain thepreset number of K-space data of the image to be reconstructed.

Preferably, image compensation unit 23 comprises:

Low-rank component calculation unit 331 is used to calculate thelow-rank component of the image to be reconstructed according to thecompensated image, the preset singular value thresholding operator andthe preset initial value of the sparse component of the image to bereconstructed; and

Sparse component calculation unit 332 is used to update the sparsecomponent of the image to be reconstructed according to the compensatedimage, the low-rank component of the image to be reconstructed and thepreset soft thresholding operator.

In the embodiment of the invention, the scanning speed and imaging speedof magnetic resonance imaging are accelerated through variable-densityundersampling, parameters in the fitting process are added to thereconstruction process to guide the reconstruction of the image to bereconstructed, and the reconstruction process and the fitting process ofmagnetic resonance imaging are connected, which effectively improves theaccuracy and efficiency of magnetic resonance imaging.

In the embodiment of the invention, each unit of the magnetic resonanceimaging device can be realized by corresponding hardware or softwareunit. Each unit may be an independent software or hardware unit, or aintegrated software and hardware unit, which shall not be used to limitthe invention.

Embodiment 3

FIG. 4 shows the structure of the medical device provided in embodiment3 of the invention. For the convenience of illustration, only partsrelated to the embodiments of the invention are shown.

The medical device 4 in the embodiment of the invention comprises aprocessor 40, a memory 41, and a computer program 42 stored in thememory 41 and capable of running on the processor 40. The processor 40executes the computer program 42 to implement the steps in the aforesaidembodiment of the method, such as steps S101 to S110 shown in FIG. 1.Or, the processor 40 executes the computer program 42 to implement thefunctions of the units in the aforesaid embodiment of the device, suchas functions of units 21 to 25 shown in FIG. 2.

In the embodiment of the invention, the scanning speed and imaging speedof magnetic resonance imaging are accelerated through variable-densityundersampling, parameters in the fitting process are added to thereconstruction process to guide the reconstruction of the image to bereconstructed, and the reconstruction process and the fitting process ofmagnetic resonance imaging are connected, which effectively improves theaccuracy and efficiency of magnetic resonance imaging.

Embodiment 5

The embodiment of the invention provides a computer readable storagemedium which stores a computer program, which stores a computer program,which is executed by the processor to implement steps in the aforesaidembodiment of the method, such as, steps S101 to S110 shown in FIG. 1.Or the computer program is executed by the processor to implement thefunctions of the units in the aforesaid embodiment of the device, suchas functions of units 21 to 25 shown in FIG. 2.

In the embodiment of the invention, the scanning speed and imaging speedof magnetic resonance imaging are accelerated through variable-densityundersampling, parameters in the fitting process are added to thereconstruction process to guide the reconstruction of the image to bereconstructed, and the reconstruction process and the fitting process ofmagnetic resonance imaging are connected, which effectively improves theaccuracy and efficiency of magnetic resonance imaging.

The computer readable storage medium in the embodiment of the inventionmay include any entity or device capable of carrying computer programcode, or recording medium, such as ROM/RAM, disk, optical disk and flashmemory, etc.

The above description is only an embodiment of the present invention; itis not intended to limit the scope of the invention. Any modification,equivalent substitution and improvement with the spirit and principle ofthis invention shall be included in the scope of protection of theinvention.

1. A magnetic resonance imaging method, wherein the steps comprise:conducting accelerated sampling on the image of the preset observedtarget to be reconstructed under the preset parameter direction toobtain the corresponding K-space data of the said image to bereconstructed; calculating the parameter value and compensationcoefficient of the said image to be reconstructed according to the saidK-space data and the preset parametric relaxation model; generating thecorresponding compensated image of the said image to be reconstructedaccording to the said compensation coefficient and calculate thelow-rank component and the sparse component of the said image to bereconstructed according to the said compensated image; updating thecompensated image according to the said low-rank component and the saidsparse component, and judge whether the update of the said compensatedimage converges. If the update converges, updating the said image to bereconstructed according to the said updated compensated image;otherwise, skipping to the step of calculating the low-rank componentand the sparse component of the said image to be reconstructedrespectively; judging whether the update of the said image to bereconstructed converges. If the update converges, fitting and outputtingthe parameter map of the said observed target according to the saidparametric relaxation model and the updated image to be reconstructed,otherwise, calculating the parameter value and compensation coefficientof the updated image to be reconstructed according to the saidparametric relaxation model, and skipping to the step of generating thecorresponding compensated image of the said image to be reconstructedaccording to the said compensation coefficient.
 2. The said method as inclaim 1, wherein the steps of conducting accelerated sampling on theimage of the preset observed target to be reconstructed under the presetparameter direction comprise: conducting full sampling in thefrequency-encoding direction and variable-density undersampling in thephase-encoding direction of the said observed object to obtain thepreset number of K-space data of the said image to be reconstructed. 3.The said method as in claim 1, wherein the steps of calculating theparameter value and compensation coefficient of the said image to bereconstructed according to the said K-space data and the presetparametric relaxation model comprise: converting the full sampling partof the said K-space data center to the image domain to obtain thecorresponding image of the full sampling part of the said K-space datacenter; Fitting to obtain the parameter value of the said image to bereconstructed according to the said corresponding image of the fullsampling part and the said parametric relaxation model, and calculatingthe said compensation coefficient according to the parameter value ofthe said image to be reconstructed.
 4. The said method as in claim 1,wherein the steps of calculating the low-rank component and the sparsecomponent of the said image to be reconstructed according to the saidcompensated image comprise: calculating the low-rank component of thesaid image to be reconstructed according to the said compensated image,the preset singular value thresholding operator and the preset initialvalue of the sparse component of the said image to be reconstructed;updating the sparse component of the said image to be reconstructedaccording to the said compensated image, the low-rank component of thesaid image to be reconstructed and the preset soft thresholdingoperator.
 5. The said method as in claim 1, wherein the steps ofupdating the said compensated image according to the said low-rankcomponent and sparse component comprise: Updating the data fidelity termof the said compensated image according to the said low-rank componentand the sparse component. The updating formula is: U_(j)^(i)=L_(j)+S_(j)−C(E*(EC⁻¹(L_(j)+S_(j)))−d), where, U_(j) ^(i) is thesaid compensated image obtained after the j^(th) update of the saidcompensated image in the i^(th) update of the said image to bereconstructed, L_(j) is the said low-rank component in the j^(th) updateof the said compensated image, S_(j) is the said sparse component in thej^(th) update of the said compensated image, E is the preset encodingmatrix, E* is the conjugate transpose of the said E, E(X)=d, X is theimage sequence composed of the said image to be reconstructed.
 6. Amagnetic resonance imaging device, wherein the device comprises:Accelerated sampling unit is used to conduct accelerated sampling on theimage of the preset observed target to be reconstructed under the presetparameter direction to obtain the corresponding K-space data of the saidimage to be reconstructed; Coefficient calculation unit is used tocalculate the parameter value and compensation coefficient of the saidimage to be reconstructed according to the said K-space data and thepreset parametric relaxation model; Image compensation unit is used togenerate the corresponding compensated image of the said image to bereconstructed according to the said compensation coefficient andcalculate the low-rank component and the sparse component of the saidimage to be reconstructed according to the compensated image; Imageupdate unit is used to update the compensated image according to thesaid low-rank component and the said sparse component, and judge whetherthe update of the said compensated image converges. If the updateconverges, the said image to be reconstructed is updated according tothe said updated compensated image; otherwise, the image compensationunit is triggered to execute the step of calculating the low-rankcomponent and the sparse component of the said image to be reconstructedrespectively; and Fitting and outputting unit is used to judge whetherthe update of the said image to be reconstructed converges. If theupdate converges, the parameter map of the said observed target isfitted and output according to the said parametric relaxation model andthe said updated image to be reconstructed, otherwise, the parametervalue and compensation coefficient of the updated image to bereconstructed is calculated according to the said parametric relaxationmodel, and the said image compensation unit is triggered to execute thestep of generating the corresponding compensated image of the said imageto be reconstructed according to the said compensation coefficient. 7.The said device as in claim 6, wherein the accelerated sampling unitcomprises: Variable-density undersampling unit is used to conduct fullsampling in the frequency-encoding direction and variable-densityundersampling in the phase-encoding direction of the said observedobject to obtain the preset number of K-space data of the said image tobe reconstructed.
 8. The said device as in claim 6, wherein the imagecompensation unit comprises: Low-rank component calculation unit is usedto calculate the low-rank component of the said image to bereconstructed according to the said compensated image, the presetsingular value thresholding operator and the preset initial value of thesparse component of the said image to be reconstructed; and Sparsecomponent calculation unit is used to update the sparse component of thesaid image to be reconstructed according to the said compensated image,the low-rank component of the said image to be reconstructed and thepreset soft thresholding operator.
 9. A medical device, which comprisesa memory, a processor, and a computer program stored in the memory andcapable of running on the said processor, wherein the steps comprise anyof the said methods as in claim 1 when the said processor executes thesaid computer program.
 10. A computer readable storage medium, whichstores a computer program, wherein the steps comprise any of the saidmethods as in claim 1 when the said computer program is executed by theprocessor.